# AinnoSeg: Panoramic Segmentation with High Perfomance Source: [AinnoSeg: Panoramic Segmentation with High Perfomance]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:28:00.017Z Authors: Jiahong Wu, Jianfei Lu, Xinxin Kang, Yiming Zhang, Yinhang Tang, Jianfei Song, Ze Huang, Shenglan Ben, Jiashui Huang, Faen Zhang Published: 2020-07-21T04:16:46.000Z ## Abstract Panoramic segmentation is a scene where image segmentation tasks is more difficult. With the development of CNN networks, panoramic segmentation tasks have been sufficiently developed.However, the current panoramic segmentation algorithms are more concerned with context semantics, but the details of image are not processed enough. Moreover, they cannot solve the problems which contains the accuracy of occluded object segmentation,little object segmentation,boundary pixel in object segmentation etc. Aiming to address these issues, this paper presents some useful tricks. (a) By changing the basic segmentation model, the model can take into account the large objects and the boundary pixel classification of image details. (b) Modify the loss function so that it can take into account the boundary pixels of multiple objects in the image. (c) Use a semi-supervised approach to regain control of the training process. (d) Using multi-scale training and reasoning. All these operations named AinnoSeg, AinnoSeg can achieve state-of-art performance on the well-known dataset ADE20K. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()